If you spend enough time talking to product leaders and CTOs in the Independent Software Vendors (ISV) space, a pattern starts to emerge. The conversation is no longer about whether to use AI or not but about how AI, via embedded software,  can be leveraged to create tangible value for clients and drive business.

There’s also a quiet shift happening which people are noticing. ISVs that are seeing results aren’t the ones chasing flashy demos or generic chat interfaces (haven’t you gotten tired of those already?) but those applying AI to solve very specific, high-friction, problems for their customers.

From what I’ve seen, four use cases consistently stand out. Not because they’re trendy, but because they solve problems customers are willing to pay for.

1. Intelligent Document Processing: Turning Unstructured Data into Action

One of the biggest misconceptions about AI is that it starts with models. In reality, it starts with information / data —and let’s be real, most of the information/data tends to be a mess (different formats, versions and type of documents across the board).  Invoices, contracts, reports, and images are everywhere, and they are mostly unstructured. Humans can read them easily, but software historically couldn’t. That gap is where a lot of inefficiency lives but also where opportunities are.

What I find interesting is how many ISVs are quietly building strong businesses around Intelligent Document Processing (IDP). They’re not trying to “revolutionize AI.” They’re simply helping their customers extract key information and move it into workflows where it can be used. Think about something as simple as invoice processing. Without AI, it’s manual, slow, and error prone. With AI, the data is captured, validated, and pushed downstream automatically. What used to take hours becomes seconds while using less resources. The real value here isn’t the extraction itself. It’s what happens after: approvals get faster, errors go down, and organizations gain visibility into processes that were previously opaque.

For ISVs, this is a powerful place to play because the ROI is immediate and there is a real actual need for it.

2. AI-Powered Knowledge Discovery: Making Information Actually Usable

Every organization I’ve worked with has the same problem: they have more information than ever, and yet people still can’t find what they need. Rings a bell? I know it does. It’s all there: buried in shared drives, email threads, CRMs, document repositories.

This is where AI-powered knowledge discovery is becoming essential. Not just search in the traditional sense, but systems that actually understand context and intent, which can actually makes a difference. Instead of asking users to hunt for information, these systems surface answers. They connect dots across sources. They reduce the time it takes to go from “I need to know this” to “Here’s what matters.”, which is the type of value clients and users are looking for.

What’s interesting is that this use-case becomes exponentially more valuable as data grows. The more content an organization has, the harder it becomes to navigate and the more impactful AI becomes. Hear me out here: think about how much information your company manages compared to five (5) years ago. Isn’t it overwhelming? I know it is.

For ISVs, who on daily basis embed software into their solutions, using this capability into their platforms changes the nature of the product. It’s no longer just a system of record. It becomes a system of insight and knowledge.

3. Named Entity Recognition: Understanding the Meaning Behind Data

Extracting data is one thing. Understanding it is quite another beast to manage. This is where Named Entity Recognition (NER) quietly does a lot of heavy lifting. It’s not always the most visible feature, but it’s often the one that makes everything else possible, especially in the times of AI.

When an application can automatically identify things like people, organizations, locations, financial terms, or legal clauses, it begins to “understand” the content it’s working with. That understanding unlocks a lot of possibilities. Documents can be classified automatically. Sensitive information can be flagged or redacted. Workflows can be triggered based on what’s detected, not just where it’s stored.

I’ve seen ISVs use this to transform compliance processes, accelerate contract analysis, and even improve search relevance. The common thread is that they’re moving from raw data to structured, meaningful information and becoming safer and more compliant with security and privacy regulations.

What’s compelling here is that NER doesn’t have to be a standalone feature. In many cases, it works best as an embedded capability that enhances everything else—search, automation, analytics.

It’s one of those building blocks that, once in place, makes the entire product smarter and more secure, especially on industries where security and the management of critical information is key. Think of healthcare, government, financial institutions and many more. Wouldn’t you want to make a difference and provide them with the power of AI, but in a secure and risk free environment? I would guess you are nodding, signaling a rotund “yes”.

4. Fraud Detection and Risk Analysis: AI Where the Stakes Are High

Some AI use cases are about efficiency. Others are about risk. Fraud detection sits firmly in the second category.  In industries like finance, insurance, and healthcare, the cost of getting it wrong is significant. A missed fraud signal isn’t just an inconvenience; it’s a direct financial loss, or worse, a compliance issue. Can you recall the number of times you have read headlines with the reference to “Fraud or Breach” in the past five years? Scary, isn’t it?

What AI brings to the table is the ability to spot patterns humans can’t easily see, especially in real time. Instead of relying solely on rules, which are static and predictable, AI models can adapt. They can detect anomalies, flag unusual behavior, and continuously learn from new data.

What I find particularly interesting is how this capability is being embedded directly into ISV platforms. It’s not an external system anymore; it’s part of the core product experience.  For customers, that means decisions can be made faster and with greater confidence. For ISVs, it creates a strong value proposition—because you’re not just offering functionality, you’re offering protection and peace of mind.

All organizations wish to reduce risk. All of them wish to be aligned with regulations and compliance. Wouldn’t you want to be the one providing the solution to fill those concerns?

Final Thoughts

When I look at the ISVs that are succeeding with AI, I don’t see them trying to do everything at once. In fact, it’s often the opposite.  They pick a specific problem—usually one that’s expensive, manual, or risky—and they apply AI in a way that removes friction by embedding software with “must have functionalities”.  That’s really the common thread across all five use cases. They’re not about showcasing AI. They’re about making work easier, faster, and more reliable. And in the end, that’s what customers care about.  Not the technology itself, but what it enables.

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